Open-source tool that maps what concepts an LLM has learned into browsable tree structures using hyperbolic geometry

I built HyperSAE, an open-source interpretability tool that extracts what an LLM "knows" and organizes it into tree-shaped concept maps. The idea: LLMs learn concepts hierarchically. "Programming" contains "Python" contains "list comprehensions." But current interpretability tools dump everything into a flat, unstructured list of thousands of features with no organization. HyperSAE uses a branch of geometry where space expands exponentially (like tree branches do), so the extracted features naturally self-organize into parent-child hierarchies. You can browse what the model learned as a navigable tree instead of scanning a flat feature list. Tested on Google's Gemma-2-2B model. Captures 99.8% of the model's learned features (compared to 96.2% with standard tools). It's a Python library: pip install hypersae GitHub: https://github.com/vishal-dehurdle/hypersae Paper with interactive demos: https://vishalvermalabs.com/papers/empirical-validation-hypersae-poincare-geometry/